One of the main goals of the digital transformation of modern enterprises is to increase the automation of their business processes. Leveraging advanced digital technologies such as Robotics Process Automation (RPA) and Artificial Intelligence (AI), enterprises can nowadays simplify and streamline complex business processes. In several cases they can also boost the quality of their processes and accelerate their delivery. In these ways, enterprises can realize significant cost savings, while freeing up human resources for more complex tasks that require human oversight and intelligence.
Once upon a time business process automation referred to the deployment of industrial automation systems such as SCADA (Supervisory Control and Data Acquisition) systems and PLCs (Programmable Logic Controllers), which enabled the automated control of industrial processes in settings like manufacturing shopfloors, energy plants and oil refineries. Such automation platforms leverage cyber-physical systems as mediators between field controllers and digital terminals. However, the scope of business process automation is no longer restricted to industrial hardware and related automation systems. Nowadays, enterprises are offered with opportunities to deploy software bots in order to automate, streamline and control processes that involve enterprise software systems such as Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM) systems. Such bots are in several cases coined as RPA agents and can be used in many different enterprise settings beyond legacy industrial processes.
RPA processes leverage cutting edge digital technologies such as Machine Learning (ML) and AI to automate tedious and repetitive workflows in enterprise environments. In this direction, RPA software operates on well-structured and standardized inputs, which facilitate the streamlining and automation of complex business logic. The most typical RPA use cases involve transaction processing, data processing and manipulation, responses’ automation, as well as information exchange between different digital platforms. The simplest RPA scenarios involve construction and dispatch of automated responses to e-mails or other messages. Nevertheless, there are much more complex RPA use cases that entail the deployment of tens of bots that automate complex processes across ERP, CRM, Supply Chain Management (SCM) and knowledge management systems. Leveraging such complex workflows, enterprises can streamline enterprises processes and reduce costs. In several cases, RPA processes are combined with AI-based software agents (e.g., chatbots) to scale up processes (e.g., customer service) without any essential increase in the respective costs. Furthermore, modern RPA bots comprise ML algorithms that enable non-deterministic processing of inputs and boost process intelligence. Therefore, RPA is also a way to accelerate ML and AI adoption by modern enterprises. In most cases, the deployment of RPA processes provides a smooth path to AI deployments, as they enable enterprises to improve their AI knowhow and to experience tangible benefits of AI automation in practice.
In recent years, CIO (Chief Information Officers) are increasingly turning to RPA towards one or more of the following benefits:
To successfully deploy process automation enterprises must consider technology, organization, and management aspects simultaneously i.e., as part of an integrated and holistic approach. Here are some relevant guidelines:
The advent of AI and RPA processes provides unprecedented innovation and efficiency opportunities for companies that are willing to deploy and fully leverage automation process. Enterprises must become prepared to exploit these opportunities based on a disciplined consideration of related management, organizational and technology issues. In this context, the above-listed guidelines provide a starting point for developing an effective strategy for AI and RPA adoption.
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